logLikelihood.celda_CG: Calculate Celda_CG log likelihood

In compbiomed/celda: CEllular Latent Dirichlet Allocation

Description

Calculates the log likelihood for user-provided cell population and feature module clusters using the 'celda_CG()' model.

Usage

logLikelihood.celda_CG(counts, sample.label, z, y, K, L, alpha, beta, delta,
  gamma)

Arguments

counts	Integer matrix. Rows represent features and columns represent cells.
sample.label	Vector or factor. Denotes the sample label for each cell (column) in the count matrix.
z	Numeric vector. Denotes cell population labels.
y	Numeric vector. Denotes feature module labels.
K	Integer. Number of cell populations.
L	Integer. Number of feature modules.
alpha	Numeric. Concentration parameter for Theta. Adds a pseudocount to each cell population in each sample. Default 1.
beta	Numeric. Concentration parameter for Phi. Adds a pseudocount to each feature module in each cell population. Default 1.
delta	Numeric. Concentration parameter for Psi. Adds a pseudocount to each feature in each module. Default 1.
gamma	Numeric. Concentration parameter for Eta. Adds a pseudocount to the number of features in each module. Default 1.
...	Additional parameters.

Value

The log likelihood for the given cluster assignments

See Also

'celda_CG()' for clustering features and cells

Examples

loglik = logLikelihood(celda.CG.sim$counts, model="celda_CG", 
                       sample.label=celda.CG.sim$sample.label,
                       z=celda.CG.sim$z, y=celda.CG.sim$y,
                       K=celda.CG.sim$K, L=celda.CG.sim$L,
                       alpha=celda.CG.sim$alpha, beta=celda.CG.sim$beta,
                       gamma=celda.CG.sim$gamma, delta=celda.CG.sim$delta)

compbiomed/celda documentation built on May 25, 2019, 3:58 a.m.